Super-resolution reconstruction of lung medical images integrating biological vision mechanisms.

In this paper, a Gabor detail feature-enhanced super-resolution network is constructed concerning the biological vision mechanism to address the problem on loss of texture detail information and contextual information in the process of medical image super-resolution. The network infrastructure is a generative adversarial network consisting of a dense residual network generator and a dual-path U-Net discriminator. The Gabor Detail feature extract Module (GDfeM) is designed to address the problem of texture detail information loss by simulating the working mechanism of simple cells. Then, for the problem of insufficient texture detail generation ability of the generator, one of the convolutional blocks of the dense residual network is replaced by GDfeM; for the problem of insufficient texture detail feature extraction ability of the discriminator, aligning the structure of the main pathway U-Net, several GDfeMs and ordinary convolutional kernels are connected to build a detail feature extraction pathway in series, This pathway is connected in parallel with the main pathway through skip connections to form a dual-pathway discriminator. Meanwhile, to address the problem of missing contextual information in the process of super-resolution reconstruction of medical images, a context loss function is introduced to make the network focus on the contextual structural information of the image to reduce distortion and artifacts. Experiments show that our method has